DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Jaa-Yeon | ko |
dc.contributor.author | Yoon, Min A | ko |
dc.contributor.author | Chee, Choong Guen | ko |
dc.contributor.author | Cho, Jae Hwan | ko |
dc.contributor.author | Park, Jin Hoon | ko |
dc.contributor.author | Park, Sung-Hong | ko |
dc.date.accessioned | 2023-09-18T08:00:59Z | - |
dc.date.available | 2023-09-18T08:00:59Z | - |
dc.date.created | 2023-09-18 | - |
dc.date.issued | 2022-09 | - |
dc.identifier.citation | 5th International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, pp.44 - 52 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10203/312712 | - |
dc.description.abstract | Our research aims to accelerate Slice Encoding for Metal Artifact Correction (SEMAC) MRI using multi-contrast deep neural networks for patients with degenerative spine diseases. To reduce the scan time of SEMAC, we propose multi-contrast deep neural networks which can produce high SEMAC factor data from low SEMAC factor data. We investigated acceleration in k-space along the SEMAC encoding direction as well as phase encoding direction to reduce the scan time further. To leverage the complementary information of multi-contrast images, we downsampled the data at different k-space positions. The output of multi-contrast SEMAC reconstruction provided great performance for correcting metal artifacts. The developed networks potentially enable clinical use of SEMAC in a reduced scan time with reasonable quality. | - |
dc.language | English | - |
dc.publisher | Springer Science and Business Media Deutschland GmbH | - |
dc.title | Metal Artifact Correction MRI Using Multi-contrast Deep Neural Networks for Diagnosis of Degenerative Spinal Diseases | - |
dc.type | Conference | - |
dc.identifier.wosid | 000867627500005 | - |
dc.identifier.scopusid | 2-s2.0-85140490697 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 44 | - |
dc.citation.endingpage | 52 | - |
dc.citation.publicationname | 5th International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 | - |
dc.identifier.conferencecountry | SI | - |
dc.identifier.conferencelocation | Singapore | - |
dc.identifier.doi | 10.1007/978-3-031-17247-2_5 | - |
dc.contributor.localauthor | Park, Sung-Hong | - |
dc.contributor.nonIdAuthor | Yoon, Min A | - |
dc.contributor.nonIdAuthor | Chee, Choong Guen | - |
dc.contributor.nonIdAuthor | Cho, Jae Hwan | - |
dc.contributor.nonIdAuthor | Park, Jin Hoon | - |
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